Monitoring Design Elements that Produce Measurable Results: How are you doing?

Thursday, 04/18/2019
1:00 PM EST - 2:30 PM EST

Event Info

Presenter: Barb Horn, River Watch of Colorado, and Sam Briggs, Izaak Walton League of America
Summary of Webinar:This webinar will guide you through a process, which evaluates an existing monitoring program or plans to create a program against key elements that produce measurable results. Regardless of program age, goals or data objectives, participants will learn the value of planning, how they are doing and what they need to do in addition to resources available to assist.

Registration Link: Coming soon.

Introducing the Water Data Collaborative

The Water Data Collaborative is an affiliation of academic, governmental and non-profit institutions dedicated to harnessing the power of citizen-based water quality monitoring data. The Collaborative has two main goals:
  • Increase the adoption of best practices and technologies among water data collectors to increase contributions of water data to state regulatory programs and open-data repositories through the Internet of Water.
  • Develop and implement technology that enables shared water data to become actionable information for water restoration and protection.
The Collaborative was formed with funding from Pisces Foundation in late 2017. Since then, we have been working to develop a systematic process and set of resources that citizen monitors can use to more effectively collect, manage, and share their data in open data repositories so the data can be used to inform decisions and take action. We invite you to join the following webinar series that will highlight some of the work we have been doing for your information as well as your feedback.

This figure shows the locations of organizations, governments, and tribes that work to protect water resources. Citizen-led non-governmental organizations make up the vast majority of these organizations. Most of these organizations participate in water quality monitoring of some kind.